feat: brain service — self-contained second brain knowledge manager
Full backend service with: - FastAPI REST API with CRUD, search, reprocess endpoints - PostgreSQL + pgvector for items and semantic search - Redis + RQ for background job processing - Meilisearch for fast keyword/filter search - Browserless/Chrome for JS rendering and screenshots - OpenAI structured output for AI classification - Local file storage with S3-ready abstraction - Gateway auth via X-Gateway-User-Id header - Own docker-compose stack (6 containers) Classification: fixed folders (Home/Family/Work/Travel/Knowledge/Faith/Projects) and fixed tags (28 predefined). AI assigns exactly 1 folder, 2-3 tags, title, summary, and confidence score per item. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Yusuf Suleman
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services/brain/app/worker/__init__.py
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services/brain/app/worker/__init__.py
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services/brain/app/worker/tasks.py
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services/brain/app/worker/tasks.py
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"""Background worker tasks — processes items after creation."""
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import asyncio
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import logging
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import uuid
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from datetime import datetime
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from redis import Redis
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from rq import Queue
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from sqlalchemy import select
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from sqlalchemy.orm import selectinload
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from app.config import REDIS_URL, DATABASE_URL_SYNC
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from app.models.item import Item, ItemAsset
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log = logging.getLogger(__name__)
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# RQ queue
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_redis = Redis.from_url(REDIS_URL)
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queue = Queue("brain", connection=_redis)
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def enqueue_process_item(item_id: str):
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"""Enqueue a background job to process an item."""
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queue.enqueue(process_item_job, item_id, job_timeout=300)
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def process_item_job(item_id: str):
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"""Synchronous entry point for RQ — runs the async pipeline."""
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asyncio.run(_process_item(item_id))
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async def _process_item(item_id: str):
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"""Full processing pipeline for a saved item."""
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from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine, async_sessionmaker
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from app.config import DATABASE_URL
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from app.services.ingest import fetch_url_content, take_screenshot, archive_html
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from app.services.classify import classify_item
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from app.services.embed import generate_embedding
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from app.search.engine import index_item, ensure_meili_index
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engine = create_async_engine(DATABASE_URL, echo=False)
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Session = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
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async with Session() as db:
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# Load item
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result = await db.execute(
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select(Item).options(selectinload(Item.assets)).where(Item.id == item_id)
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)
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item = result.scalar_one_or_none()
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if not item:
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log.error(f"Item {item_id} not found")
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return
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try:
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item.processing_status = "processing"
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await db.commit()
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extracted_text = item.raw_content or ""
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title = item.title
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html_content = None
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# ── Step 1: Fetch content for URLs ──
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if item.type == "link" and item.url:
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log.info(f"Fetching URL: {item.url}")
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content = await fetch_url_content(item.url)
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html_content = content.get("html")
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extracted_text = content.get("text") or extracted_text
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if not title:
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title = content.get("title")
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item.metadata_json = item.metadata_json or {}
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item.metadata_json["description"] = content.get("description")
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item.metadata_json["used_browserless"] = content.get("used_browserless", False)
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# Take screenshot
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screenshot_path = await take_screenshot(item.url, item.id)
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if screenshot_path:
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asset = ItemAsset(
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id=str(uuid.uuid4()),
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item_id=item.id,
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asset_type="screenshot",
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filename="screenshot.png",
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content_type="image/png",
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storage_path=screenshot_path,
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)
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db.add(asset)
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# Archive HTML
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if html_content:
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html_path = await archive_html(html_content, item.id)
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if html_path:
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asset = ItemAsset(
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id=str(uuid.uuid4()),
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item_id=item.id,
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asset_type="archived_html",
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filename="page.html",
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content_type="text/html",
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storage_path=html_path,
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)
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db.add(asset)
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# ── Step 2: AI classification ──
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log.info(f"Classifying item {item.id}")
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classification = await classify_item(
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item_type=item.type,
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url=item.url,
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title=title,
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text=extracted_text,
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)
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item.title = classification.get("title") or title or "Untitled"
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item.folder = classification.get("folder", "Knowledge")
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item.tags = classification.get("tags", ["reference", "read-later"])
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item.summary = classification.get("summary")
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item.confidence = classification.get("confidence", 0.0)
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item.extracted_text = extracted_text
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# ── Step 3: Generate embedding ──
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log.info(f"Generating embedding for item {item.id}")
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embed_text = f"{item.title or ''}\n{item.summary or ''}\n{extracted_text}"
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embedding = await generate_embedding(embed_text)
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if embedding:
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item.embedding = embedding
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# ── Step 4: Update status ──
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item.processing_status = "ready"
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item.updated_at = datetime.utcnow()
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await db.commit()
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# ── Step 5: Index in Meilisearch ──
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log.info(f"Indexing item {item.id} in Meilisearch")
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await ensure_meili_index()
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await index_item({
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"id": item.id,
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"user_id": item.user_id,
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"type": item.type,
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"title": item.title,
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"url": item.url,
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"folder": item.folder,
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"tags": item.tags or [],
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"summary": item.summary,
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"extracted_text": (extracted_text or "")[:10000], # Truncate for search index
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"processing_status": item.processing_status,
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"created_at": item.created_at.isoformat() if item.created_at else None,
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})
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log.info(f"Item {item.id} processed successfully")
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except Exception as e:
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log.error(f"Processing failed for item {item.id}: {e}", exc_info=True)
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item.processing_status = "failed"
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item.processing_error = str(e)[:500]
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item.updated_at = datetime.utcnow()
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await db.commit()
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await engine.dispose()
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